11/29/22 Forbes – Does Ethical AI Development Rely On The “Algorithmically” Underserved? CHAI’s Mission

CHAI co-founders Dr. Halamka and Dr. Anderson and CHAI member Suchi Saria of Bayesian Health discuss the importance and timeliness of CHAI’s mission, and share how the organization plans to prioritize patient safety, reliability, equity, transparency, and trust in the healthcare AI development process.

“AI as a field is evolving very rapidly. As a result, there is variable expertise amongst groups in how to go about implementing it correctly and evaluating whether what they’ve implemented is working. There is significant opportunity to accelerate AI adoption by sharing best practices and developing guardrails that the broader community (government, payor and provider groups) can benefit from.” Dr. Suchi Saria

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Read more about Bayesian Health’s approach to Adaptive AI

11/20/22 Healthcare In Europe – Early detection of sepsis with the help of AI

Suchi Saria, PhD, director of the Machine Learning and Healthcare Lab at Johns Hopkins, who led this work, explains that TREWS automatically and continuously monitors disparate clinical data, including vital signs, laboratory data, medication history, procedure and clinical history, and physician notes. It generates a continuous real-time “sepsis score” that can trigger an alert to healthcare staff. Clinical caregivers can analyse why the TREWS alert was generated, accept or dismiss it, and initiate timely treatment on patients confirmed to be septic.

‘Our results showing high physician adoption and associated mortality and morbidity reductions are a milestone for the field of AI,’ comments Saria. ‘They are the culmination of nearly a decade of significant technological investment, deep collaboration, the development of novel techniques, and rigorous evaluation. Further, what’s most exciting here is that this approach is applicable not just to sepsis but to many other critical complications.’

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Read more about Bayesian Health’s approach to Adaptive AI

10/16/22 The Atlantic – Doctors Still Struggle to Diagnose a Condition That Kills More Americans Than Stroke

In July, Johns Hopkins researchers published a trio of studies in Nature Medicine and npj Digital Medicine showcasing an early-warning system that uses artificial intelligence. The system caught 82 percent of sepsis cases and significantly reduced mortality. While AI—in this case, machine learning—has long promised to improve health care, most studies demonstrating its benefits have been conducted using historical data sets. Sources told me that, to the best of their knowledge, when used on patients in real time, no AI algorithm has shown success at scale.

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Learn more about Bayesian’s peer-reviewed research on sepsis here

09/26/22 AMA – What it takes for doctors to trust AI-triggered sepsis alerts

Increasing adoption in sepsis AI with Bayesian. “This is a breakthrough in many ways,” said study co-author Albert W. Wu, MD, director of the Center for Health Services and Outcomes Research at Johns Hopkins Bloomberg School of Public Health. “Up to this point, most of these types of systems have guessed wrong much more often than they get it right. Those false alarms undermine confidence.”

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Learn more about Bayesian’s peer-reviewed research on sepsis here

08/11/22 Becker’s Hospital Review – Early warnings, few false alerts: What physicians want out of AI sepsis detection tools

Mitigating sepsis risk with AI machine learning. “This tool makes every clinician more efficient and can let you manage more patients because there’s less work to do,” said Lee Sacks, MD, founding CEO of Advocate Physician Partners and former system chief medical officer of Downers Grove, Ill., and Milwaukee-based Advocate Aurora Health. “So I think, going forward … [staff] are going to choose to work in organizations that have a tool like this because it makes them more effective.”

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Read the Becker’s article

Learn more about Bayesian’s peer-reviewed research on sepsis here